System and method of detecting abnormal movement of a physical object

ABSTRACT

The present application discloses a method of detecting abnormal movement of a physical object. A periodic signal is representative of the movement of the object. According to some embodiments, a raw matrix having a first array and a second array is generated, and then an integrated matrix is generated by performing a dimension reduction on the raw matrix. A likelihood of a predetermined type of abnormal movement of the physical object is determined by comparing the integrated matrix with a predetermined benchmark pattern. In some embodiments, the generation of the raw matrix includes performing a first analysis on a predetermined portion of the periodic signal to generate the first array and performing a second analysis different from the first analysis on the predetermined portion of the periodic signal to generate the second array.

BACKGROUND

Some physical objects incorporate or encompass cyclic or periodicmotion, such as an electrical motor having a rotor that spins at arotational speed or a human heart beating (i.e., performing contractionand relaxation) at a heart rate. The cyclic movement of the physicalobjects is observable or recordable by detection systems in the form ofperiodic or substantially periodic signals. The term “periodic orsubstantially periodic signals” (hereinafter also referred to as“periodic signals”) refers to the nature of the detected signals thatusually have repetitive nominal waveform patterns although the exactwaveforms and frequencies vary. Abnormal movement of a given physicalobject is thus detectable by analyzing the periodic signals.

For example, an Electrocardiograph (ECG) device is capable of convertingthe movement of a heart into one or more ECG signals from one or morecombinations of leads attached to a person or animal undergoingexamination. A trained medical care provider may identify certainabnormal movement(s) of the observed heart by comparing the ECG signalswith a benchmark ECG signal of normal movement.

SUMMARY

In the present application, a method of detecting abnormal movement of aphysical object is disclosed. According to some embodiments, a periodicsignal is representative of movement of the physical object, and themethod includes: generating a raw matrix comprising a first array and asecond array; generating an integrated matrix by performing a dimensionreduction on the raw matrix; and determining a likelihood of apredetermined type of abnormal movement of the physical object bycomparing the integrated matrix or a set of indexes derived from theintegrated matrix with a predetermined benchmark pattern correspondingto the predetermined type of abnormal movement.

In some embodiments, the generation of the raw matrix includes:performing a first analysis on a predetermined portion of the periodicsignal to generate the first array, the predetermined portioncorresponding to a predetermined time period of the periodic signal; andperforming a second analysis different from the first analysis on thepredetermined portion of the periodic signal to generate the secondarray.

In some embodiments, the first analysis or the second analysis is atime-domain analysis, a pattern analysis, or deriving a feature from thepredetermined duration of the periodic signal obtained according to afirst spatial measurement configuration and at least a portion ofanother periodic signal being representative of the movement obtainedaccording to a second spatial measurement configuration.

In the present application, a method of detecting abnormality in apredetermined portion of an electrocardiography (ECG) signalcorresponding to a predetermined time period is disclosed. In someembodiments, the method includes: segmenting the predetermined durationof ECG signal into a plurality of ECG segments, each ECG segmentincluding a nominal ECG pattern; for adjacent ECG segments, re-samplingat least one of the adjacent ECG segments to derive two ECG arrayshaving the same number of data points; for adjacent ECG segments,calculating a joint probability and a marginal probability of the twoECG arrays of the adjacent ECG segments; generating a mutual informationarray based on the calculated joint probability and the calculatedmarginal probability of adjacent ECG segments; and determining alikelihood of a predetermined type of illness by comparing the mutualinformation array or a set of indexes derived from the mutualinformation array with a predetermined benchmark pattern correspondingto the predetermined type of illness.

In some embodiments, the calculation of the mutual information array isperformed based on an equation of:

${{MI}\left( {X;Y} \right)} = {\sum\limits_{x}{\sum\limits_{y}{{p\left( {x,y} \right)}\log \; \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}}}$

where x and y each represents components of one of the two ECG arrays ofthe adjacent ECG segments, p(x,y) represents the joint probability ofthe two ECG arrays, and p(x) and p(y) each represents the marginalprobability of one of the two ECG arrays of the adjacent ECG segments.

In the present application, an abnormality analysis system forperforming the disclosed methods and computer readable storage mediumbeing encoded with a computer program code which when executed by aprocessor causes the processor to perform the disclosed methods are alsodisclosed.

As will be realized, one or more embodiments are capable of other anddifferent embodiments, and the several details are capable ofmodification in various obvious respects, all without departing from thedescribed embodiments.

DESCRIPTION OF THE DRAWINGS

One or more embodiments are illustrated by way of example, and not bylimitation, in the figures of the accompanying drawings, whereinelements having the same reference numeral designations represent likeelements throughout and wherein:

FIG. 1 is a plot of a waveform corresponding to an ECG signal;

FIG. 2 is a flow chart of various methods of analyzing the waveform ofthe ECG signal of FIG. 1 in conjunction with some embodiments;

FIG. 3 is a flow chart of a method of analyzing periodic signalsrepresenting movement of a physical object according to someembodiments;

FIG. 4 is a flow chart of a method of detecting abnormal movement of aphysical object according to some embodiments;

FIG. 5 is a functional block diagram of an abnormality analysis systemusable for implementing the method disclosed in FIGS. 2-4 according tosome embodiments;

FIGS. 6A and 6B are charts of resulting data arrays of various analysesperformed on a benchmark ECG signal, integrated matrices, and anannotated waveform corresponding to abnormal movement according to someembodiments;

FIGS. 7A and 7B are charts of resulting data arrays of performingasymmetric index (AI) of multi-scale analysis on results of RR intervalanalysis and an integrated matrix derived from ECG signals of end-stagerenal disease (ESRD) patients according to some embodiments.

DETAILED DESCRIPTION

FIG. 1 is a plot of a waveform 100 corresponding to an ECG signal of ahuman heart where the signal has been segmented and categorized intovarious segments (intervals) and feature points. Some of the segmentsand feature points of the ECG signal include P-wave 110, T-wave 120, QRScomplex 130 that further includes a Q-point 132, an R-point 134, and anS-point 136, a PR-segment 140, and a ST-segment 150. A complete cardiaccycle C thus includes a P-wave 110 section, a PR-segment 140, a Q-point132, an R-point 134, an S-point 136, a ST-segment 150, and a T-wave 120section. In some embodiments, more segments and/or feature points aredefined for various information processing purposes.

ECG signal sections and feature points are defined in order tofacilitate the analysis of heart movement. In practice, illness ordisease symptoms affect the rhythms or patterns of heart movement andare identifiable by analyzing ECG signal waveforms 100. Although thecorrelation between a given type of illness and the ECG signal waveforms100 is usually identifiable, the given type of illness is more readilyidentifiable from resulting signals after performing a data analysis onECG signal waveforms 100 particularly for emphasizing the correlationbetween a given illness type and the resulting signals.

FIG. 2 is a flow chart of various example methods of analyzing the ECGsignal. The process of performing an analysis on the ECG signal in orderto generate resultant data is also referred to as feature extraction. Insome embodiments, the disclosed analyses are performed by a computersystem or an ECG equipment executing a software program, i.e., a set ofexecutable/interpretable instructions. It is understood that in someembodiments only some of the disclosed analyses are performed foranalyzing ECG signals for a patient. In some embodiments, a personhaving ordinary skill in the art will appreciate that additionaloperations are performed before, during, and/or after the method of FIG.2.

In operation 202, an ECG signal such as the example signal depicted bywaveform 100 in FIG. 1 is obtained through ECG transducers or stored ECGdata transmitted from a storage device or via a network. Then, inoperation 204, the cardiac cycles C of the ECG signal waveform 100 areidentified. In some embodiments, the identification of cardiac cycles Cincludes first detecting some of the feature points. For example, in atleast one embodiment, the cardiac cycles C of the ECG signal waveform100 are identified according to detection of R-points 134 in the ECGsignal waveform 100.

In some embodiments, at least one time-domain analysis 210 is performedon the ECG signal waveform 100. In at least one embodiment, afterdetection of R-points 134, the time intervals between two adjacentR-points (RR interval or RRI) T_(RR) (FIG. 1) are calculated inoperation 211. The RR intervals T_(RR) represent the periods of cardiaccycles C (also referred to as NN intervals), i.e., the inverseinformation of a heart rate. Then, a statistical analysis is performedon the calculated RR intervals T_(RR) in operation 212. In someembodiments, the calculated RR intervals T_(RR) are analyzed accordingto one or more of the following approaches: standard deviation of NNintervals (SDNN), standard deviation of average NN intervals (SDANN),the root-mean-square of successive differences of RR intervals (RMSSD),the number of pairs of successive NN intervals that differ by more than50 ms (NN50), the proportion of NN50 divided by total number of NNintervals (pNN50), etc.

In at least another embodiment, after detection of R-points 134, otherfeature points and sections, such as Q-points 132, S-points 136, P-wave110, and T-wave 120, are also detected in operation 213. Then, PRintervals T_(PR) (FIG. 1) and QT intervals T_(QT) (FIG. 1) arecalculated in operation 214, and either one or both of these twointervals are informative features for representing characteristics of acardiac cycle C. Finally, in operation 215, a statistical analysissimilar to the methods described above for operation 212 is performed onthe calculated PR intervals T_(PR), and the QT intervals T_(QT).

In some embodiments, at least one morphology analysis 230 is performedon the ECG signal waveform 100. A morphology analysis refers to ananalysis based on the waveforms or patterns of the examined signal. Inat least one embodiment, after detection of R-points 134, other featurepoints and sections, such as Q-points 132, S-points 136, P-wave 110, andT-wave 120, are also detected and identified in operation 232.Subsequently in operation 234, features such as slopes of ST segment150, T-wave 120, P-wave 110, or other features are derived from thevariance of patterns of recorded cardiac cycles C.

In at least one embodiment, in operation 236, the morphology analysis230 includes extracting features derived from the variance betweenadjacent cardiac cycles C of the ECG signal waveform 100. In at leastanother embodiment, the morphology analysis 230 performed in operation238 includes a method of evaluating morphology variance between twoadjacent cardiac cycles C of the ECG signal waveform 100 by calculatingmutual information based on joint and marginal probabilities.

In some embodiments, one or more other types of analyses 240 areperformed on the ECG signal waveform 100. For example, in someembodiments after detection of R-points 134, features concerningelectrical axes of a heart are calculated in operation 242 based onpositive/negative waves at feature points (such as P-wave 110, Q-point132, R-point 134, S-point 136, T-wave 120, etc.) from ECG signalsreceived from a different combination of leads.

FIG. 3 is a flow chart of a method of analyzing periodic signalsrepresenting movement of a physical object according to someembodiments. The analysis method depicted in FIG. 3 is a method basedupon morphology of the examined substantially periodic signal, such asthe ECG signal waveform 100 analyzed according to operation 238 in FIG.2. It is understood that in some embodiments additional operations areperformed before, during, and/or after the method of FIG. 3.

In operation 310, a predetermined portion of the substantially periodicsignal corresponding to a predetermined time period is segmented into aplurality of signal segments, each signal segment including a nominalwaveform. For example, in at least one embodiment for which apredetermined duration of the ECG signal waveform 100 is being analyzed,the nominal waveform is the waveform pattern corresponding to a cardiaccycle C, and the segmentation is performed by dissecting the ECG signalwaveform 100 in between adjacent R-points. In some embodiments, thenominal waveform is the waveform defined between adjacent R-points 134,and the segmentation is performed by dissecting the ECG signal waveform100 at R-points 134.

The analyzed substantially periodic signal, such as an ECG signalwaveform 100 in some embodiments, is a discrete-time signal and thus thesignal segments are also sequences of data points or data arrays.However, because the effective frequency of the analyzed substantiallyperiodic signal varies even within the time period of the predeterminedportion of the substantially periodic signal, the size of the signalsegments is not necessarily the same. Therefore, in operation 320, twosignal arrays in adjacent signal segments having the same size areobtained by re-sampling one or both of the adjacent signal segments.“Re-sampling” refers to re-creation of a continuous waveform based on anoriginal data array and deriving a new data array from the re-createdcontinuous waveform, and thus the new data array and the original dataarray both represent the same continuous waveform. In some embodiments,the re-sampling is performed by interpolation or extrapolation of theoriginal data array in a linear or polynomial manner or other applicablecurve-fitting algorithms.

For example, in some embodiments, one of two signal segmentscorresponding to neighboring cardiac cycles C are re-sampled in order togenerate two corresponding signal arrays having the same size. If, forexample, after segmentation, a first cardiac cycle C includes 262 datapoints, and a second cardiac cycle C includes 274 data points, eitherone of the first cardiac cycle or the second cardiac cycle is re-sampledto match the size, i.e., same number of data points, of the othercardiac cycle. In some embodiments, the second cardiac cycle isre-sampled to generate a signal array having the same size as the firstcardiac cycle, e.g., said 262 data points.

In operation 330, after the re-sampling for adjacent signal segments thejoint probability and the marginal probability of the two signal arraysof the adjacent signal segments are calculated. In some embodiments, thejoint probability and marginal probability are calculated according tothe two re-sampled signal arrays X and Y of ECG signals of adjacentperiods (i.e., adjacent signal segments corresponding to neighboringcardiac cycles C). Elements of the signal array X or signal array Yrepresent magnitudes of the ECG signals. The marginal probability ofeach signal array (X or Y) is determined by first accumulating thecounts of each different value of elements in the signal array, and thencalculating the proportion of each cumulative count to the total amountof elements in the signal array to obtain the marginal probabilities ofeach different value of elements p(x) or p(y). Considering bothsequences X and Y together, similar to the calculation of marginalprobability, the joint probability p(x,y) of two events x and y inconjunction is determined. Then, in operation 340, a mutual informationindex is calculated based on the calculated joint probability and thecalculated marginal probability of every adjacent signal segments. In atleast one embodiment, the calculation of the mutual information index isperformed based on application of the following equation:

${{MI}\left( {X;Y} \right)} = {\sum\limits_{x}{\sum\limits_{y}{{p\left( {x,y} \right)}\log \; \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}}}$

X and Y represent components of one of the two signal arrays of theadjacent signal segments, p(x,y) represents the joint probability of thetwo signal arrays, and p(x) and p(y) each represents the marginalprobability of one of the two signal arrays of the adjacent signalsegments.

In operation 350, a mutual information array is generated based on thecalculated mutual information indexes. For example, the mutualinformation array includes an array of the calculated mutual informationindexes listed based on their sequence in the examined ECG signalwaveform 100. The generated mutual information array is usable forfurther information processing. In at least one embodiment, thegenerated mutual information array is compared with a predeterminedbenchmark pattern corresponding to one or more predetermined types ofillness in order to determine the likelihood of the one or morecorresponding predetermined types of illness.

Although the method depicted in FIG. 3 is explained using ECG signals asan example, a person having ordinary skill in the art will appreciatethat the same analysis method is usable for analyzing substantiallyperiodic signals other than ECG signals. In some embodiments, thesubstantially periodic signal to be analyzed is obtained by detectingone of the following activities: heartbeat, breathing, speech,earthquake or seismic activity, orbital movement of an astronomicalobject, periodic variances of an astronomical object, movement of apiston, or rotation of a motor. Also, the analysis method of FIG. 3 isusable to detect a likelihood of a predetermined type of abnormalmovement of the physical object by comparing the mutual informationmatrix with a predetermined benchmark pattern corresponding to thepredetermined type of abnormal movement.

FIG. 4 is a flow chart of a method of detecting abnormal movement of aphysical object according to some embodiments. The method depicted inFIG. 4 is explained in the context of analyzing an ECG signal waveform100 as depicted in FIG. 1. It is understood that, in some embodiments,the method depicted in FIG. 4 is usable for analyzing various types ofperiodic signals representing movement of a physical object. A personhaving ordinary skill in the art will appreciate that in someembodiments additional operations are performed before, during, and/orafter the method of FIG. 4.

In general, the method depicted in FIG. 4 incorporates results from twoor more different analyses or feature extraction methods, such as theones disclosed in FIG. 2, into an integrated feature matrix in order toconsolidate information for further analysis, such as risk assessmentbased on an ECG signal or abnormal movement detection of a physicalobject, while reducing the total amount of data.

In operation 410, a predetermined portion of a substantially periodicsignal is obtained and pre-processed. In at least one embodiment foranalyzing an ECG signal, a predetermined period of ECG signals isobtained by an ECG transducer to record electrical potential differenceof heart muscle cells caused by electrical pulses of a heart that isbeing observed. In some embodiments, more ECG transducers or the sametransducer with leads attached to different portions of a human bodyare, used to provide electrical axis information or other information.In yet some other embodiments where other biological or non-biologicalsignals are to be analyzed, applicable detecting systems or transducersother than ECG transducers are used.

In some embodiments, the detected ECG signal is further amplified and/orlevel-shifted to have signal levels of the ECG signal adjusted to bewithin a predetermined range for further analog-to-digital conversionand/or filtering. In at least one embodiment, the detected ECG signalsare also affected by breathing or factors not relevant to the heartactivities. Therefore, the contribution of noise or other component inthe detected ECG signal from irrelevant factors is suppressed in orderto obtain a filtered ECG signal, such as the example ECG signal depictedin FIG. 1, for subsequent information processing.

After obtaining the predetermined portion of the substantially periodicsignal to be analyzed, a raw matrix comprising at least two data arraysderived based on different signal analyses is generated.

For example, in operation 420, a first analysis is performed on thepredetermined portion of the substantially periodic signal to generate afirst array, and a second analysis different from the first analysis isperformed on the predetermined portion of the substantially periodicsignal to generate a second array. In some embodiments, more than twodifferent analyses are performed and more than two resulting data arraysare generated.

In some embodiments analyzing a non-ECG signal, the first analysis orthe second analysis is a time-domain analysis, a morphology analysis, apattern analysis, or derivation of a feature from the predeterminedduration of the substantially periodic signal obtained according to afirst spatial measurement configuration and at least a portion ofanother substantially periodic signal is representative of the movementobtained according to a second spatial measurement configuration.

In some other embodiments analyzing an ECG signal waveform 100, thefirst analysis or the second analysis is a time-domain analysis, amorphology analysis, an electric-axis analysis. For example, two or moreof the following analysis are performed: RR Interval analysis (e.g.,operations 211/212), PR-QT interval analysis (e.g., operation213/214/215), morphology feature analysis (e.g., operation 232/234),morphology distance analysis (e.g., operation 236), mutual informationanalysis (e.g., method depicted in FIG. 3), electric axis analysis(e.g., operation 242), or other available ECG signal analyses.

In operation 430, a raw matrix is generated according to the results ofthe analysis performed on the periodic signal, such as the firstanalysis and the second analysis selected from one of the ECG signalanalysis methods depicted in FIGS. 2 and 3, as well as other applicableECG or biology signal analysis methods. In some embodiments, resultsfrom a third or more different analyses are incorporated in the rawmatrix.

Subsequently in operation 440, an integrated feature matrix having adimension no greater than the dimension of the raw matrix is generatedby performing a dimension reduction on the raw matrix. In someembodiments, the dimension reduction is performed by applying principalcomponent analysis, factor analysis, or independent component analysison the raw matrix.

In operation 450, the likelihood of a predetermined type of abnormalmovement of the physical object is determined by comparing theintegrated matrix with a predetermined benchmark pattern correspondingto the predetermined type of abnormal movement. For example, if ECGsignals of a heart are analyzed, the abnormal activities of the heartbeing examined are identified, and the likelihood of a predeterminedtype of illness correlated to the abnormal activities is assessedaccordingly.

Although the method depicted in FIG. 4 is explained with reference toanalyzing ECG signals, the same analysis method is usable for analyzingsubstantially periodic signal other than ECG signals. In someembodiments, the substantially periodic signal to be analyzed isobtained by detecting one of the following activities: heartbeat,breathing, speech, earthquake or seismic activity, orbital movement ofan astronomical object, periodic variances of an astronomical object,movement of a piston, or rotation of a motor. Also, the analysis methodof FIG. 4 is usable to detect likelihood of a predetermined type ofabnormal movement of the physical object by comparing the integratedfeature matrix with a predetermined benchmark pattern corresponding tothe predetermined type of abnormal movement.

For example, in some embodiments that analyze a predetermined durationof speech for recognizing the acoustic characters of the speech, thepredetermined duration of speech signal is segmented into a plurality ofwindows, and each window includes one or more periods of speechwaveforms. The mutual information analysis method of FIG. 3 is usable toobtain a mutual information array representing the variations amongdifferent windows. Further, together with data arrays derived by usingother analysis methods such as the ones similar to the methods depictedin blocks 210/230/240 of FIG. 2, an integrated matrix is derived usingthe method of FIG. 4 in order to suppress noises for subsequent speechprocessing.

FIG. 5 is a functional block diagram of an abnormality analysis systemusable for implementing the method disclosed in FIGS. 2-4 according tosome embodiments.

Abnormality analysis system 500 includes a computer system 510comprising a computer readable storage medium 512 encoded with, i.e.,storing, a computer program code, i.e., a set of executableinstructions. The computer system 510 includes a processor 514electrically coupled to the computer readable storage medium 512. Theprocessor 514 is configured to execute or interpret the computer programcode encoded in the computer readable storage medium 512 in order tocause the computer to function as a signal analyzer for performing theabnormality analysis and risk assessment for the substantially periodicsignal to be examined, such as an ECG signal, as depicted in FIGS. 2-4.

In some embodiments, the processor 514 is a central processing unit(CPU), a multi-processor, a distributed processing system, and/or anysuitable processing unit. In at least one embodiment, the processor 514acquires information such as the predetermined duration of periodicsignal, the predetermined benchmark pattern, and/or other informationfrom the memory storage medium 512.

In some embodiments, the computer readable storage medium 512 is anelectronic, magnetic, optical, electromagnetic, infrared, and/or asemiconductor system (or apparatus or device). For example, the computerreadable storage medium 512 includes a semiconductor or solid-statememory, a magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk, and/or anoptical disk. In some embodiments using optical disks, the computerreadable storage medium 512 includes a compact disk-read only memory(CD-ROM), a compact disk-read/write (CD-R/W), and/or a digital videodisc (DVD).

Further, the computer system 510 includes an input/output interface 516and a display 518. The input/output interface 516 is coupled to theprocessor 514 and allows an operator or a medical care professional tooperate the computer system 510 in order to perform the methods depictedin FIGS. 2-4. The display 518 displays the status of operation of themethods depicted in FIGS. 2-4 in a real-time manner, and preferablyprovides a Graphical User Interface (GUI). The input/output interface516 and the display 518 allow an operator to operate the computer system512 in an interactive manner.

The computer system 510 also includes a network interface 522 coupled tothe processor 514. The network interface 522 allows the computer system510 to communicate with a network 530, to which one or more othercomputer systems are connected. The network interface 522 includeswireless network interfaces such as BLUETOOTH, WIFI, WIMAX, GPRS, orWCDMA; or wired network interface such as ETHERNET, USB, or IEEE-1394.In some embodiments, the method of FIGS. 2-4 are implemented in two ormore computer systems 510 of FIG. 5, and information such as thepredetermined duration of periodic signal, the predetermined benchmarkpattern, and/or other information are exchanged between differentcomputer systems via the network 530.

In at least one embodiment, the abnormality analysis system 500 furthercomprises a transducer 540. The transducer 540 is capable of observingthe physical object to be examined and converting the movement of thephysical object into a representative signal. In some embodimentsanalyzing ECG signals, the transducer 540 observes the heart to beexamined and converts the muscle movement of the heart into ECG signals.

The computer system 510 further has an interface 524 coupled to thetransducer 540 and the processor 514. The interface 524 bridges thetransducer 540 with the processor 514 and outputs the picked up periodicsignals in discrete-time signal format. For example, if the transducer540 picks up an ECG signal, the interface receives the ECG signal fromthe transducer 540 and outputs the ECG signal in the format of a ECGdata array to the processor 514. In some embodiments, the transducer 540converts one of the following physical phenomenon into electricalsignals: heartbeats, breathing, speech, earthquakes, orbital movement ofan astronomical object, periodic variances of an astronomical object,movement of a piston, or rotation of a motor.

FIG. 6A is a chart of resulting data arrays 612/614 of two differentanalyses performed on a benchmark ECG signal representing abnormalmovement of a heart, an integrated matrix 616 according to the resultsof the two different analyses, and an annotated waveform 618 identifyingwindows 622˜628 for pulses corresponding to abnormal movement accordingto some embodiments.

The example depicted in FIG. 6A uses at least two different ECG featureextraction methods: time-domain RR Interval analysis and morphologydistance analysis. The testing is performed based on arrhythmia databaseof Massachusetts Institute of Technology and Boston's Beth IsraelHospital (MIT-BIH), which is an international standard database.Comparing the result of RR Interval analysis 612 with the annotatedwaveform 618, application of the RR Interval analysis on the examinedECG signal reveals abnormal movement corresponding to the abnormalityidentified at windows 622 and 628. However, the result of RR Intervalanalysis 612 fails to identify the abnormal movement corresponding tothe abnormality identified at windows 624 and 626. In the same example,the result of morphology distance analysis 614, compared with theannotated waveform 618, reveals abnormal movement corresponding to theabnormality identified at windows 626. However, the result of morphologydistance analysis 614 fails to identify the abnormal movementcorresponding to the abnormality identified at windows 622, 624, and628.

After information integration and dimension reduction in accordance withthe method depicted in FIG. 4, the information relevant to identifyingabnormal movement corresponding to the abnormality identified at windows622, 626, and 628 from resulting arrays 612 and 614 are integrated intothe integrated feature matrix 616. In at least one embodiment, theintegrated feature matrix 616 is a one-by-N array derived from a rawmatrix, which is a two-by-N matrix including two one-by-N arrays (theresulting arrays 612 and 614). N is the number of data points in theresulting arrays 612 and 614.

Therefore, a single integrated feature matrix 616 is usable foridentifying the abnormality identifiable by the resulting arrays 612 and614. That is, the integrated matrix 616 integrates and preservesinformation in the resulting arrays 612 and 614 relevant to subsequentabnormality detection while reducing the overall volume of information,and thus to improve the computation efficiency in subsequentdetermination of abnormal movement.

FIG. 6B is a chart of resulting data arrays 612/614/632 of threedifferent analyses performed on a benchmark ECG signal representingabnormal movement of a heart, an integrated matrix 634 according to theresults of the three different analyses, and an annotated waveform 618identifying windows 622˜628 for pulses corresponding to abnormalmovement according to some embodiments. The testing is also performedbased on the MIT-BIH arrhythmia database. In addition to the results ofRR Interval analysis 612 and morphology distance analysis, the resultingdata array 632 is derived based on mutual information analysis asdepicted in FIG. 3. Compared with the annotated waveform 618, applyingmutual information analysis reveals abnormal movement corresponding tothe abnormality identified at windows 622 and 628, and also at window624, which is not easily detectable from solely the results of RRInterval analysis 612 and morphology distance analysis 614. Although,the result of mutual information analysis 632 fails to identify theabnormal movement corresponding to window 626, mutual informationanalysis helps to remedy the deficiencies of the RR Interval analysisand the morphology distance analysis.

After information integration and dimension reduction as depicted inFIG. 4, the information relevant to identifying positions 622, 624, 626,and 628 from resulting arrays 612, 614, and 632 are integrated into theintegrated feature matrix 634. In at least one embodiment, theintegrated feature matrix 634 is a one-by-N array derived from a rawmatrix, which is a three-by-N matrix including three one-by-N arrays(the results 612, 614, and 632). N being the number of data points inthe resulting data arrays 612, 614, and 632. Therefore, a singleintegrated feature matrix 634 is usable for identifying the abnormalmovement identifiable by the resulting arrays 612, 614, and 632 by usinga reduced-size matrix. That is, the integrated feature matrix 634integrates and preserves information relevant to subsequent abnormalitydetection while reducing the overall volume of information, and thus toimprove the computation efficiency in subsequent determination ofabnormal movement.

FIG. 7A is a chart of resulting data arrays of performing asymmetricindex (AI) of multi-scale analysis on results of RR interval analysis ofECG signals obtained from observing End Stage Renal Disease (ESRD)patients, with or without diabetes mellitus (DM), according to someconfigurations. It is known to the applicants that ESRD patients'conditions regarding DM are discernable by Glycated hemoglobin (HbA1c)tests. While ESRD patients with DM demonstrate HbA1c greater than 5.7,ESRD patients without DM demonstrate HbA1c less than 5.7. As depicted inFIG. 7A, results of performing the AI analysis based on results of RRinterval analysis for ESRD patients with DM 712 and ESRD patientswithout DM 714 are not helpful in distinguishing these two differentgroups.

FIG. 7B is a chart of resulting data arrays of performing AI ofmulti-scale analysis on an integrated feature matrix based on results ofRR interval analysis, morphology distance analysis and mutualinformation analysis of ECG signals obtained from observing ESRDpatients, with or without DM, according to some embodiments. As depictedin FIG. 7B, results of performing the AI based on results of integratedmatrix for ESRD patients with DM 722 and without DM 724 fall indifferent ranges, and thus the analysis is helpful in distinguishingthese two different groups. Thus, compared with performing analysisdepicted in FIG. 2 individually, in some embodiments, the analysismethod as depicted in FIG. 4 not only incorporates information fromvarious analyses, but also enhances the correlation between the resultsof the analysis, i.e., the integrated feature matrix, and one or morepatterns corresponding to particular illness or abnormality. Moreover,compared with tests that require blood-drawing and days of laboratorytest, analyzing ECG signals is a relatively less invasive and moretime-efficient approach in distinguishing ESRD patients with and withoutDM.

The foregoing outlines features of several embodiments so that thoseskilled in the art may better understand the aspects of the presentdisclosure. Those skilled in the art should appreciate that they mayreadily use the present disclosure as a basis for designing or modifyingother processes and structures for carrying out the same purposes and/orachieving the same advantages of the embodiments introduced herein.Those skilled in the art should also realize that such equivalentconstructions do not depart from the spirit and scope of the presentdisclosure, and that they may make various changes, substitutions, andalterations herein without departing from the spirit and scope of thepresent disclosure.

1. A method of detecting abnormal movement of a physical object, aperiodic signal being representative of movement of the physical object,the method comprising: generating a raw matrix comprising a first arrayand a second array, the generation of the raw matrix comprising:performing a first analysis on a predetermined portion of the periodicsignal to generate the first array, the predetermined portioncorresponding to a predetermined time period of the periodic signal; andperforming a second analysis different from the first analysis on thepredetermined portion of the periodic signal to generate the secondarray; generating an integrated matrix by performing a dimensionreduction on the raw matrix; and determining a likelihood of apredetermined type of abnormal movement of the physical object bycomparing the integrated matrix or a set of indexes derived from theintegrated matrix with a predetermined benchmark pattern correspondingto the predetermined type of abnormal movement.
 2. The method of claim1, wherein the first analysis or the second analysis is a time-domainanalysis, a pattern analysis, or deriving a feature from thepredetermined duration of the periodic signal obtained according to afirst spatial measurement configuration and at least a portion ofanother periodic signal being representative of the movement obtainedaccording to a second spatial measurement configuration.
 3. The methodof claim 1, wherein the first analysis is a mutual information analysis,comprising: segmenting the predetermined duration of the periodic signalinto a plurality of signal segments, each signal segment including anominal waveform; for every adjacent signal segments, obtaining twosignal arrays having the same size by re-sampling one or both of theadjacent signal segments; for every adjacent signal segments,calculating joint probability and marginal probability of the two signalarrays of the adjacent signal segments; and calculating a mutualinformation index based on the calculated joint probability and thecalculated marginal probability of every adjacent signal segments. 4.The method of claim 3, wherein the calculation of the mutual informationindex is performed based on an equation of:${{MI}\left( {X;Y} \right)} = {\sum\limits_{x}{\sum\limits_{y}{{p\left( {x,y} \right)}\log \; \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}}}$where x and y each represents components of one of the two signal arraysof the adjacent signal segments, p(x,y) represents the joint probabilityof the two signal arrays, and p(x) and p(y) each represents the marginalprobability of one of the two signal arrays of the adjacent signalsegments.
 5. The method of claim 1, wherein the periodic signal isoutput of an electrocardiography (ECG) transducer.
 6. The method ofclaim 5, wherein the first analysis or the second analysis is atime-domain analysis, a morphology analysis, or an electric-axisanalysis.
 7. The method of claim 5, wherein the predetermined type ofabnormal movement corresponds to a predetermined illness.
 8. The methodof claim 1, wherein the dimension reduction is performed by applyingprincipal component analysis, factor analysis, or independent componentanalysis on the raw matrix.
 9. The method of claim 1, wherein the rawmatrix further comprising a third array, and the generation of the rawmatrix further comprising performing a third analysis different from thefirst analysis and the second analysis on the predetermined portion ofthe periodic signal to generate the third array.
 10. The method of claim1, wherein the periodic signal is obtained by detecting one or more ofthe following activities: heartbeat, breathing, speech, earthquake orseismic activity, orbital movement of an astronomical object, periodicvariances of an astronomical object, movement of a piston, or rotationof a motor.
 11. A method of detecting abnormality in a predeterminedportion of an electrocardiography (ECG) signal corresponding to apredetermined time period, the method comprising: segmenting thepredetermined duration of ECG signal into a plurality of ECG segments,each ECG segment including a nominal ECG pattern; for adjacent ECGsegments, re-sampling at least one of the adjacent ECG segments toderive two ECG arrays having the same number of data points; foradjacent ECG segments, calculating a joint probability and a marginalprobability of the two ECG arrays of the adjacent ECG segments;generating a mutual information array based on the calculated jointprobability and the calculated marginal probability of adjacent ECGsegments; and determining a likelihood of a predetermined type ofillness by comparing the mutual information array or a set of indexesderived from the mutual information array with a predetermined benchmarkpattern corresponding to the predetermined type of illness.
 12. Themethod of 11, wherein the calculation of the mutual information array isperformed based on an equation of:${{MI}\left( {X;Y} \right)} = {\sum\limits_{x}{\sum\limits_{y}{{p\left( {x,y} \right)}\log \; \frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}}}}$where x and y each represents components of one of the two ECG arrays ofthe adjacent ECG segments, p(x,y) represents the joint probability ofthe two ECG arrays, and p(x) and p(y) each represents the marginalprobability of one of the two ECG arrays of the adjacent ECG segments.13. The method of 11, further comprising: performing a first analysisdifferent from the generation of the mutual information array on thepredetermined duration of ECG signal to generate a first array; andgenerating an integrated matrix by performing a dimension reduction on araw matrix comprising the mutual information array and the firstanalyzed array.
 14. The method of claim 13, wherein the first analysisis a time-domain analysis, a morphology analysis, or an electric-axisanalysis.
 15. The method of claim 13, wherein the dimension reduction isperformed by applying principal component analysis, factor analysis, orindependent component analysis on the raw matrix.
 16. The method ofclaim 13, further comprising: performing a second analysis differentfrom the generation of the mutual information array on the predeterminedduration of ECG signal and the first analysis on the predeterminedduration of ECG signal array to generate a second array; wherein the rawmatrix comprising the mutual information array, the first array, and thesecond array.
 17. An abnormality analysis system comprising: a computerreadable storage medium encoded with a computer program code; aprocessor coupled to the computer readable storage medium, the processorbeing configured to execute the computer program code; wherein thecomputer program code is configured to cause the process to: receive anelectrocardiography (ECG) data array representing a predeterminedduration of activity of a heart; divide the ECG data array into aplurality of ECG segments, each ECG segment including a nominal ECGwaveform; for adjacent ECG segments, derive two same-size ECG arrays byre-sampling one or both of the adjacent ECG segments; for adjacent ECGsegments, calculate joint probability and marginal probability of thetwo same-size ECG arrays of the adjacent ECG segments; generate a mutualinformation array based on the calculated joint probability and thecalculated marginal probability of adjacent ECG segments; and determinelikelihood of abnormal movement of the heart by comparing the mutualinformation array or a set of indexes derived from the mutualinformation array with a predetermined benchmark pattern.
 18. Theabnormality analysis system of claim 17, wherein the computer programcode is further configured to cause the process to: perform a firstanalysis different from the generation of the mutual information arrayon the ECG data array to generate a first array; and generate anintegrated matrix by performing a dimension reduction on a raw matrixcomprising the mutual information array and the first array.
 19. Theabnormality analysis system of claim 17, further comprising: atransducer configured to observe the heart; and an interface coupled tothe transducer and the processor and configured to output the ECG dataarray to the processor.
 20. The abnormality analysis system of claim 17,further comprising: a network interface coupled to the processor and anetwork; wherein the computer program code is configured to cause theprocess to receive the predetermined benchmark pattern transmitted fromthe computer readable storage medium or the network.
 21. A computerreadable storage medium being encoded with a computer program code whichwhen executed by a processor causes the processor to perform a methodcomprising: receiving an electrocardiography (ECG) data arrayrepresenting a predetermined duration of activity of a heart; dividingthe ECG data array into a plurality of ECG segments, each ECG segmentincluding a nominal ECG waveform; for adjacent ECG segments, derivingtwo same-size ECG arrays by re-sampling one or both of the adjacent ECGsegments; for adjacent ECG segments, calculating joint probability andmarginal probability of the two same-size ECG arrays of the adjacent ECGsegments; generating a mutual information array based on the calculatedjoint probability and the calculated marginal probability of adjacentECG segments; and determining a likelihood of abnormal movement of theheart by comparing the mutual information array or a set of indexesderived from the mutual information array with a predetermined benchmarkpattern.
 22. The computer readable storage medium of claim 21, whereinthe computer program code is further configured to cause the process to:perform a first analysis different from the generation of the mutualinformation array on the predetermined duration of ECG signal togenerate a first array; and generate an integrated matrix by performinga dimension reduction on a raw matrix comprising the mutual informationarray and the first array.
 23. A method of identifying a predeterminedtype of abnormal movement of a physical object, a periodic signal beingrepresentative of movement of the physical object, the methodcomprising: generating a raw matrix comprising a first array and asecond array, the generation of the raw matrix comprising: performing afirst analysis on a predetermined portion of the periodic signal togenerate the first array; and performing a second analysis differentfrom the first analysis on the predetermined portion of the periodicsignal to generate the second array; generating an integrated matrix byperforming a dimension reduction on the raw matrix; and identifying thepredetermined type of abnormal movement of the physical object bycomparing the integrated matrix or a set of indexes derived from theintegrated matrix with a predetermined benchmark pattern correspondingto the predetermined type of abnormal movement.
 24. The method of claim23, wherein the identification of the predetermined type of abnormalmovement comprising determining a likelihood of the predetermined typeof abnormal movement.
 25. The method of claim 23, wherein the firstanalysis or the second analysis is a time-domain analysis, a patternanalysis, or deriving a feature from the predetermined duration of theperiodic signal obtained according to a first spatial measurementconfiguration and at least a portion of another periodic signal beingrepresentative of the movement obtained according to a second spatialmeasurement configuration.
 26. The method of claim 23, wherein thedimension reduction is performed by applying principal componentanalysis, factor analysis, or independent component analysis on the rawmatrix.
 27. The method of claim 23, wherein the periodic signal isobtained by detecting one or more of the following activities:heartbeat, breathing, speech, earthquake or seismic activity, orbitalmovement of an astronomical object, periodic variances of anastronomical object, movement of a piston, or rotation of a motor.